Determination of impact parameter in high-energy heavy-ion collisions via deep learning

In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq 12.5$ fm at $\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into appropriate input data (the energy spectra of final-state charged hadrons), we construct a deep neural network (DNN) and a convolutional neural network (CNN) to connect final-state observables with impact parameters. The results show that both the DNN and CNN can reconstruct the impact parameters with a mean absolute error about $0.4$ fm with CNN behaving slightly better. Then, we test the neural networks for different beam energies and pseudorapidity ranges in this task. It turns out that these two models work well for both low and high energies. But when making test for a larger pseudorapidity window, we observe that the CNN shows higher prediction accuracy than the DNN. With the method of Grad-CAM, we shed light on the `attention' mechanism of the CNN model

Effect of source tampering in the security of quantum cryptography

The security of source has become an increasingly important issue in quantum cryptography. Based on the framework of measurement-device-independent quantum-key-distribution (MDI-QKD), the source becomes the only region exploitable by a potential eavesdropper (Eve). Phase randomization is a cornerstone assumption in most discrete-variable (DV-) quantum communication protocols (e.g., QKD, quantum coin tossing, weak coherent state blind quantum computing, and so on), and the violation of such an assumption is thus fatal to the security of those protocols. In this paper, we show a simple quantum hacking strategy, with commercial and homemade pulsed lasers, by Eve that allows her to actively tamper with the source and violate such an assumption, without leaving a trace afterwards. Furthermore, our attack may also be valid for continuous-variable (CV-) QKD, which is another main class of QKD protocol, since, excepting the phase random assumption, other parameters (e.g., intensity) could also be changed, which directly determine the security of CV-QKD.Comment: 9 pages, 6 figure

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table

Effects of ion motion on linear Landau damping

The effects of ion motion on Landau damping has been studied by the use of one-dimensional Vlasov-Poisson simulation. It is shown that the ion motion may significantly change the development of the linear Landau damping. When the ion mass is multiple of proton mass, its motion will halt the linear Landau damping at some time due to the excitation of ion acoustic waves. The latter will dominate the system evolution at the later stage and hold a considerable fraction of the total energy in the system. With very small ion mass, such as in electron-positron plasma, the ion motion can suppress the linear Landau damping very quickly. When the initial field amplitude is relatively high such as with the density perturbation amplitude δn/n0 > 0.1, the effect of ion motion on Landau damping is found to be weak or even ignorable

Hyper-Activated Pro-Inflammatory CD16+ Monocytes Correlate with the Severity of Liver Injury and Fibrosis in Patients with Chronic Hepatitis B

BACKGROUND: Extensive mononuclear cell infiltration is strongly correlated with liver damage in patients with chronic hepatitis B virus (CHB) infection. Macrophages and infiltrating monocytes also participate in the development of liver damage and fibrosis in animal models. However, little is known regarding the immunopathogenic role of peripheral blood monocytes and intrahepatic macrophages. METHODOLOGY/PRINCIPAL FINDINGS: The frequencies, phenotypes, and functions of peripheral blood and intrahepatic monocyte/macrophage subsets were analyzed in 110 HBeAg positive CHB patients, including 32 immune tolerant (IT) carriers and 78 immune activated (IA) patients. Liver biopsies from 20 IA patients undergoing diagnosis were collected for immunohistochemical analysis. IA patients displayed significant increases in peripheral blood monocytes and intrahepatic macrophages as well as CD16(+) subsets, which were closely associated with serum alanine aminotransferase (ALT) levels and the liver histological activity index (HAI) scores. In addition, the increased CD16(+) monocytes/macrophages expressed higher levels of the activation marker HLA-DR compared with CD16(-) monocytes/macrophages. Furthermore, peripheral blood CD16(+) monocytes preferentially released inflammatory cytokines and hold higher potency in inducing the expansion of Th17 cells. Of note, hepatic neutrophils also positively correlated with HAI scores. CONCLUSIONS: These distinct properties of monocyte/macrophage subpopulations participate in fostering the inflammatory microenvironment and liver damage in CHB patients and further represent a collaborative scenario among different cell types contributing to the pathogenesis of HBV-induced liver disease